{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAsiP4mohC2_"
      },
      "source": [
        "# Gemini API: Enum Quickstart\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Enum.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lF6sWVRGQ_bi"
      },
      "source": [
        "The Gemini API allows you to supply a schema to define function arguments (for [function calling](../quickstarts/Function_calling.ipynb)), or to constrain its output in [JSON](../quickstarts/JSON_mode.ipynb) or using an Enum. This tutorial gives some examples using enums."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "857d8bf104ed"
      },
      "source": [
        "### Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qLuL9m7KhvxR"
      },
      "outputs": [],
      "source": [
        "%pip install -q -U \"google-genai>=1.0.0\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-axqBTM8Lbd"
      },
      "source": [
        "### Configure your API key\n",
        "\n",
        "To run the following cell, your API key must be stored in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see [Authentication ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](../quickstarts/Authentication.ipynb) for an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "d6lYXRcjthKV"
      },
      "outputs": [],
      "source": [
        "from google import genai\n",
        "from google.colab import userdata\n",
        "\n",
        "GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')\n",
        "client = genai.Client(api_key=GOOGLE_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RcgHcOTUL0l"
      },
      "source": [
        "## Model\n",
        "\n",
        "Now select the model you want to use in this guide, either by selecting one in the list or writing it down. Keep in mind that some models, like the 2.5 ones are thinking models and thus take slightly more time to respond (cf. [thinking notebook](./Get_started_thinking.ipynb) for more details and in particular learn how to switch the thiking off).\n",
        "\n",
        "For more information about all Gemini models, check the [documentation](https://ai.google.dev/gemini-api/docs/models/gemini) for extended information on each of them.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "kwvtS6B0UPz9"
      },
      "outputs": [],
      "source": [
        "MODEL_ID = \"gemini-2.5-flash\" # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9nIks0R-tIa"
      },
      "source": [
        "## Enums"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XM_v4-LWw4SK"
      },
      "source": [
        "In the simplest case is you need the model to choose one option from a list of choices, use an enum class to define the schema. Ask it to identify this instrument:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "3q4bnZKUyPfv"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  111k  100  111k    0     0   287k      0 --:--:-- --:--:-- --:--:--  287k\n"
          ]
        },
        {
          "data": {
            "image/jpeg": "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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x511>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "!curl -O https://storage.googleapis.com/generativeai-downloads/images/instrument.jpg\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "instrument = Image.open('instrument.jpg')\n",
        "instrument"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_avRjudJ-bL7"
      },
      "source": [
        "The response should be one of the following options:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "_s9oAOmOwgNt"
      },
      "outputs": [],
      "source": [
        "import enum\n",
        "\n",
        "class InstrumentClass(enum.Enum):\n",
        "    PERCUSSION = \"Percussion\"\n",
        "    STRING = \"String\"\n",
        "    WOODWIND = \"Woodwind\"\n",
        "    BRASS = \"Brass\"\n",
        "    KEYBOARD = \"Keyboard\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-LUNiCL--_9"
      },
      "source": [
        "Pass the enum class as the `response_schema`, and for this simplest case you can use the `response_mime_type = \"text/x.enum\"` option to get one of those enum members as the response."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "KCjKMfwuxZwm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Brass\n"
          ]
        }
      ],
      "source": [
        "from google.genai import types\n",
        "\n",
        "response = client.models.generate_content(\n",
        "    model=MODEL_ID,\n",
        "    contents=[instrument, 'what is the category of this instrument?'],\n",
        "    config=types.GenerateContentConfig(\n",
        "        response_mime_type=\"text/x.enum\",\n",
        "        response_schema=InstrumentClass\n",
        "    )\n",
        ")\n",
        "\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdYXDJWZ-ov3"
      },
      "source": [
        "You can also use enums with `response_mime_type = \"application/json\"`. In this simple case the response will be identical but in quotes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "a1s2eA-P-wof"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\"Keyboard\"\n"
          ]
        }
      ],
      "source": [
        "response = client.models.generate_content(\n",
        "    model=MODEL_ID,\n",
        "    contents=[instrument, 'what category of instrument is this?'],\n",
        "    config=types.GenerateContentConfig(\n",
        "        response_mime_type=\"application/json\",\n",
        "        response_schema=InstrumentClass\n",
        "    )\n",
        ")\n",
        "\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgZTElb4_zZm"
      },
      "source": [
        "Outside of simple multiple choice problems, an enum can be used anywhere in the schema for [JSON](../quickstarts/JSON_mode.ipynb) or [function calling](../quickstarts/Function_calling.ipynb). For example, ask it for a list of recipe titles, and use a `Grade` enum to give each one a popularity-grade:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "h2pz_-mKxpYS"
      },
      "outputs": [],
      "source": [
        "import typing_extensions as typing\n",
        "\n",
        "class Grade(enum.Enum):\n",
        "  A_PLUS = 'a+'\n",
        "  A = 'a'\n",
        "  B = 'b'\n",
        "  C = 'c'\n",
        "  D = 'd'\n",
        "  F = 'f'\n",
        "\n",
        "class Recipe(typing.TypedDict):\n",
        "  recipe_name: str\n",
        "  grade: Grade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBlWzt6M-2oM"
      },
      "source": [
        "For this example you want a list of `Recipe` objects, so pass `list[Recipe]` to the `response_schema` field of the `generation_config`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "8oe-tL8MDGtx"
      },
      "outputs": [],
      "source": [
        "result = client.models.generate_content(\n",
        "    model=MODEL_ID,\n",
        "    contents=\"List about 10 cookie recipes, grade them based on popularity\",\n",
        "    config=types.GenerateContentConfig(\n",
        "        response_mime_type=\"application/json\",\n",
        "        response_schema=list[Recipe],\n",
        "        http_options=types.HttpOptions(\n",
        "            timeout=60000  # Pass timeout as a keyword argument to HttpOptions constructor\n",
        "        )\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YuUY6fk732Jq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\n",
            "    {\n",
            "        \"recipe_name\": \"Chocolate Chip Cookies\",\n",
            "        \"grade\": \"a+\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Oatmeal Raisin Cookies\",\n",
            "        \"grade\": \"a\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Peanut Butter Cookies\",\n",
            "        \"grade\": \"a\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Sugar Cookies\",\n",
            "        \"grade\": \"a\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Snickerdoodles\",\n",
            "        \"grade\": \"a\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Macarons\",\n",
            "        \"grade\": \"b\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Shortbread Cookies\",\n",
            "        \"grade\": \"b\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Gingerbread Cookies\",\n",
            "        \"grade\": \"b\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"White Chocolate Macadamia Nut Cookies\",\n",
            "        \"grade\": \"a\"\n",
            "    },\n",
            "    {\n",
            "        \"recipe_name\": \"Biscotti\",\n",
            "        \"grade\": \"c\"\n",
            "    }\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "response = json.loads(result.text)\n",
        "print(json.dumps(response, indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "092c34759bb9"
      },
      "source": [
        "## Next Steps\n",
        "### Useful API references:\n",
        "\n",
        "Check the [structured ouput](https://ai.google.dev/gemini-api/docs/structured-output) documentation or the [`GenerationConfig`](https://ai.google.dev/api/generate-content#generationconfig) API reference for more details.\n",
        "\n",
        "### Related examples\n",
        "\n",
        "* The constrained output is used in the [Text summarization](../examples/json_capabilities/Text_Summarization.ipynb) example to provide the model a format to summarize a story (genre, characters, etc...)\n",
        "* The [Object detection](../examples/Object_detection.ipynb) examples are using the JSON constrained output to uniiformize the output of the detection.\n",
        "\n",
        "### Continue your discovery of the Gemini API\n",
        "\n",
        "An Enum is not the only way to constrain the output of the model, you can also use an [JSON](../quickstarts/Enum.ipynb) schema. [Function calling](../quickstarts/Function_calling.ipynb) and [Code execution](../quickstarts/Code_Execution.ipynb) are other ways to enhance your model by either let him use your own functions or by letting it write and run them."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Enum.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
